I think maybe this could better illustrate what can be done using DVC: https://dagshub.com/DAGsHub-Official/DAGsHub-Tutorial-MNIST https://dagshub.com/DAGsHub-O

I think maybe this could better illustrate what can be done using DVC: https://dagshub.com/DAGsHub-Official/DAGsHub-Tutorial-MNIST https://dagshub.com/DAGsHub-Official/DAGsHub-Tutorial-MNIST You can navigate branches, and be able to download the data, model, and intermediate pipeline files from a shared team AWS,GS,Hadoop, or plain NFS or SSH server, as they were in a specific commit. Also compare metrics between branches for comparison of different experiments, etc. A team member can checkout a branch, immediately get the relevant files which were already computed by someone else, modify the training code, reproduce the out-of-date parts of the pipeline using dvc repro, and then git commit the resulting metrics + dvc push the resulting model back to the shared team storage.